基于多种群粒子群算法的舰船消磁决策优化

Optimization of warship degaussing decision based on poly-population particle swarm algorithm

随着舰船消磁技术的发展,消磁线圈的数目越来越多,调整消磁系统的常规方法越来越难以实施.为解决这一问题,提出一种改进的粒子群算法,该方法利用多种群搜索策略来压缩搜索空间,从而有效提高得到全局最优解的概率.仿真结果表明:该算法在舰船消磁磁场特征均方根最小和峰值最小方面均比其他方法更有优势,此外,该算法具有计算方法直观,编程简单,计算速度快,全局解搜索率高,易于进行多机协作的优点,可以方便地应用于工程实际.

With the development of warship degaussing technology, the number of degaussing coils gets more and more, and conventional methods of calibrating degaussing systems are more and more difficult to implement. To solve this problem, an improved particle swarm algorithm is proposed, which adopts the search strategy of poly-population particle to compress search space to improve the probability of searching global optimization solution. The simulation results show that this algorithm has more advantages than others m terms of minimizing the root mean square and peak of the warship degaussing magnetic field signature. In addition, this algorithm has many excellences such as intuitional algorithm, simple program, fast computation speed and high global searching probability, which make it easy to collaborate multi-computer and be applied to real projects.